Computer vision-based crack analysis for civil infrastructure has become popular to
automatically process inspection imaging data for crack detection, localisation and …

This paper reviews recent developments in deep learning-based crack segmentation
methods and investigates their performance under the impact from different image types …

In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …

Using deep learning (DL) to recognize building and infrastructure damage via images is
becoming popular in vision‐based structural health monitoring (SHM). However, many …

As civil structures age and deteriorate, it becomes crucial to conduct structural health
monitoring (SHM) to ensure safety and timely maintenance. Surface defect detection plays a …

This paper presents an innovative method to quantify damage based on surface cracks of
reinforced concrete shear walls (RCSWs). The key idea is to use artificial intelligence and …

Over the last two decades, the integration of big data and deep learning technologies has
demonstrated remarkable effectiveness across various domains of civil engineering, leading …

Cracking is one of the typical damages in concrete structures, and it is crucial to detect and
quantify cracks in a timely and efficient manner. However, current research primarily focuses …

In‐field visual inspections have inherent challenges associated with humans such as low
accuracy, excessive cost and time, and safety. To overcome these barriers, researchers and …

This paper proposes a new deep‐learning framework for drive‐by bridge condition
monitoring. The proposed approach represents a bridge monitoring regime that enables the …